Predictive Attributes for Developing Long COVID-A Study Using Machine Learning and Real-World Data from Primary Care Physicians in Germany.

(1) In the present study, we used data comprising patient medical histories from a panel of primary care practices in Germany to predict post-COVID-19 conditions in patients after COVID-19 diagnosis and to evaluate the relevant factors associated with these conditions using machine learning methods. (2) Methods: Data retrieved from the IQVIATM Disease Analyzer database were used. Patients with at least one COVID-19 diagnosis between January 2020 and July 2022 were selected for inclusion in the study. Age, sex, and the complete history of diagnoses and prescription data before COVID-19 infection at the respective primary care practice were extracted for each patient. A gradient boosting classifier (LGBM) was deployed. The prepared design matrix was randomly divided into train (80%) and test data (20%). After optimizing the hyperparameters of the LGBM classifier by maximizing the F2 score, model performance was evaluated using several test metrics. We calculated SHAP values to evaluate the importance of the individual features, but more importantly, to evaluate the direction of influence of each feature in our dataset, i.e., whether it is positively or negatively associated with a diagnosis of long COVID. (3) Results: In both the train and test data sets, the model showed a high recall (sensitivity) of 81% and 72% and a high specificity of 80% and 80%; this was offset, however, by a moderate precision of 8% and 7% and an F2-score of 0.28 and 0.25. The most common predictive features identified using SHAP included COVID-19 variant, physician practice, age, distinct number of diagnoses and therapies, sick days ratio, sex, vaccination rate, somatoform disorders, migraine, back pain, asthma, malaise and fatigue, as well as cough preparations. (4) Conclusions: The present exploratory study describes an initial investigation of the prediction of potential features increasing the risk of developing long COVID after COVID-19 infection by using the patient history from electronic medical records before COVID-19 infection in primary care practices in Germany using machine learning. Notably, we identified several predictive features for the development of long COVID in patient demographics and their medical histories.

Analysis of Psychological Distress During COVID-19 Among Professionals

The aftermath of the lockdown caused by the current pandemic generates many challenges and opportunities for the professionals as well as for organizations. Several organizations forced the people to work on-site whereas many of the organizations have been allowing work from home. However, both ways of working are challenging and cause psychological distress. The present work analyses the psychological distress among professionals residing in India during the COVID-19 pandemic. The work considers both the scenarios of working professionals: professionals working from home and professionals working onsite. The work introduces a novel hybrid machine learning approach called GBETRR. GBETRR combines two approaches, namely gradient-boosting classifier and extra-trees regressor repressor. The present work also uses a hybrid parameter optimization algorithm. Multiple performance metrics are used to evaluate the performance evaluation. Results revealed that the professionals with work from home are more stressed as compared to the professionals working onsite. Copyright © 2022 IGI Global.

Comparison of Machine Learning Models for predicting Covid-19 patients' recovery in India

The pandemic Covid-19 is a name coined by WHO on 31st December 2019. This devastating illness was carried on by a new coronavirus known as SARS-COV-2. Most of the research has focused on estimating the total number of cases and mortality rate of COVID-19. Due to this, people across the world were stressed out by observing the growing number of cases every day. As a means of maintaining equilibrium, this paper aims to identify the best way to predict the number of recovered cases of Coronavirus in India. Dataset was divided into two parts: training and testing. The training dataset utilised 70% of the dataset, and the testing dataset utilised 30%. In this paper, we applied 10 machine learning techniques i.e. Random Forest Classifier (RF), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), Gradient Boosting Classifier (GBM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), K Neighbour Classifier (KNN), Decision Tree Classifier (DT), SVM - Linear and Ada-Boost Classifier in order to predict recovered patients in India. Our study suggests that Random Forest Classifier outperforms other machine learning models for predicting the recovered Coronavirus patients having an accuracy of 0.9632, AUC of 0.9836, Recall of 0.9640, Precision of 0.9680, F1 Score of 0.9617 and Kappa of 0.9558. © 2022 IEEE.

ANALYZING THE IMPACT OF SLEEP TRAIT IN THE DETECTION AND PREDICTION OF STRESS THROUGH MACHINE LEARNING

People are thriving towards perfection, performance, and profit in the society which inturn is leading to disturbances among them both mentally and physically. One issue faced by most of the people irrespective of the age groups is "Stress". With the onset of Covid-19 pandemic, Stress has become a disastrous disorder faced by most of the people today. Most of the people are unaware that they are suffering from such a disorder. Stress lays in the hands of at-most all people either knowingly or unknowingly. There are numerous methods to detect stress manually. People don't come forward to take up treatments for stress. This disorder peeps out of humans through various symptoms like irritation, loss of appetite, agitation, depression, anxiety, reduced performance, sleep disturbance, etc. Among the afore mentioned symptoms, sleep disturbance is the major and most influential parameter in detecting and predicting stress. The SaYo Pillow is the "Smart-Yoga Pillow" which assists in concerning the relationship pertaining to sleep and stress. Although there are other methods to track sleep like Fitbit trackers to track sleeping patterns, SaYo Pillow stands out as it detects the psychological behaviors that occurs during sleep. This tracking of psychological behavior is lacking in case of other devices like Fitbit used for sleep pattern detection. The data obtained from this pillow can be used to study how stress can affect sleep. Machine Learning methods are applied to the data to detect if the person is stressed or not. Thereby adding to it, prediction is also done to understand will the person be stressed in near future. Machine Learning algorithms such as Support Vector Machine (SVM), Random Forest Classifier and Gradient Boosting Classifier was used to detect and predict stress among the individuals. The performance of these algorithms was compared to identify the best performing algorithms. After identifying the best performing algorithm, the same was applied to the data to detect and predict the occurrence of stress. In addition to that, an application was developed which suggests some activities to the candidate to overcome stress. © 2022 University of Stockholm. All rights reserved.

Predicting Susceptibility to Covid Stress Using Data Mining

Coronavirus (COVID-19) had major impacts on the daily lives of people. Lock-downs, work from home situations, loss in jobs, market changes, and less communication, and interaction between people especially during the stressful Covid period have made them more vulnerable to mental health issues, depression, loneliness, etc. With Covid related healthcare being given priority, the mental health issues faced by the public that has been both directly and indirectly affected by it have been majorly left ignored. These issues need to be taken care of by people on individual level and by the government for better public health. Hence, in this paper we introduce the emerging technique of data mining into the Covid-19 linked mental health for predicting the susceptibility of the general public around the globe to mental health side effects as a result of covid and pandemic circumstances. We used the COVIDiSTRESS survey data containing 103825 instances of people across the globe to identify the people more susceptible to Covid related stress. Logistic regression, random forest, xgboost, AdaBoost, and gradient boosting classifier were applied to the processed data giving an accuracy of 88.12%, 88.89%, 88.73%, 88.60%, and 89.25% respectively. The Models predicted the people who are likely to face covid stress based on different independent factors like their demographic variables, trust of authorities, corona concerns etc. The stress factor was measured using PSS-10 variable included in the survey. The result showed that the model developed with Gradient Boosting Classifier is found to be the most efficient model with an accuracy of 89.25%. Our analysis also showed that females, divorced/widowed people and full-time employees were more prone to stress amongst others in the gender/marital status/employment category. © 2022 IEEE.